Ingrid Van Keilegom is a Full Professor at the Faculty of Economics and Business (FEB) at KU Leuven. She is a member of the LISS – KU Leuven Institute of Sports Science and serves as a program director for the POC Master in Statistics. Senior academic staff member of FEB Council and Campus Council Observer in Faculty of Science Council Member of LStat General Assembly Her research focuses on Survival Analysis and Nonparametric Regression , with expertise in: Dependent Censoring Cure Models Measurement Error Correction Quantile Regression Multivariate Statistical Inference Causal Surrogacy Metrics Recent publications emphasize copula-based methods, quantile regression for censored data, and applications to biomedical and economic forecasting. Key projects include instrumental variable approaches for endogeneity in cure models, bankruptcy prediction via machine learning, and measurement error modeling in multivariate settings. Notable affiliations: Centre for Efficiency and Productivity Analysis Collaborations in pharmaceutical statistics and actuarial science
Hacer Atar Yıldız is an Associate Professor at the Department of Electronics and Communication Engineering, Faculty of Electrical and Electronics Engineering at Istanbul Technical University (ITU). She holds a B.Sc. (1997) and M.Sc. (2000) in Electronics Engineering from Karadeniz Technical University, and a Ph.D. (2015) in Electronics Engineering from ITU. Her research focuses on analog circuit design, integrated circuits, analog filters, memristor structures, and graphene sensors. Education: Ph.D. in Electronics Engineering (2015), Istanbul Technical University M.Sc. in Electronics Engineering (2000), Karadeniz Technical University B.Sc. in Electronic Communication Engineering (1997), Karadeniz Technical University German Language Education (2001), Munich Technical University Research Interests: Her work emphasizes innovative analog circuit solutions, including memristor-based systems, neural networks, and sensor technologies. Notable contributions include memcapacitor/meminductor emulator circuits and cryogenic bandgap designs. She also explores applications in plant identification using copula models and thermal compensation for microbolometers. Professional Experience: Associate Professor at ITU (2022–present) Researcher at Virginia University (2018) Expert Engineer at Türk Telekom (2003–2009) Intern at Marco GmbH (Munich, 2001–2002) Teaching: She has taught courses such as Introduction to Electronics, Electronic Design, and Analog Circuits at both undergraduate and graduate levels. Recent courses include EHB 222E and EHB 335. Languages & Hobbies: Fluent in English and German. Enjoys swimming, long-distance running, Turkish folk music, and outdoor activities.
Aleksandar Mijatović is a Professor of Probability at the Department of Statistics, University of Warwick, and Deputy Head of Department for Research. He was previously Chair in Probability at King's College London and Reader in Probability at Imperial College London. His research focuses on probability theory, stochastic processes, mathematical finance, numerical stochastics, and data science. He holds a Ph.D. in low-dimensional topology from Trinity College Cambridge and worked as a quantitative analyst in foreign exchange derivatives before academia. Research interests include stochastic analysis of processes with jumps, simulation methods (e.g., Monte Carlo), stochastic control, and applications in finance. He is a Fellow of the Alan Turing Institute and maintains a YouTube channel, Prob-AM, explaining his research. His work often bridges theoretical probability with practical applications in finance and data science. Key publications explore topics like reflected Brownian motion, Lévy processes, branching processes, and stochastic gradient descent. Collaborations with institutions like King’s College London and Imperial College London highlight his academic networks. His contributions span theoretical advancements and computational methodologies, with applications in risk management, option pricing, and algorithm development.
Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.
Ralf Wilke is a Professor in Applied Econometrics and Microeconometrics at the Copenhagen Business School , with affiliations to ZEW as a research associate. His career spans multiple institutions including the University of Leicester, University of Nottingham, and University of York. Education: Diplom-Volkswirt (Bonn University), DEA (University of Toulouse I), Dr. rer. pol. (Dortmund University) Current Roles: Professor at CBS, Research Associate at ZEW Prior Roles: Lecturer/Associate Professor at Leicester/Nottingham, Readership at York Research Focus: Specializing in econometric methods for analyzing duration data, competing risks, panel models with group structures, and applications in industrial life modeling. His work addresses complex censoring mechanisms and spatial diffusion patterns in historical economic contexts. Recent Publication Trends: Recent articles emphasize duration modeling with dependent censoring, copula-based competing risks frameworks, and structural econometric approaches to panel data. Key disciplines include computational statistics, quality engineering, and cliometrics.
Dimitris Karlis is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), within the School of Information Sciences and Technology. He has been a key academic figure since earning his BSc and PhD in Statistics from AUEB in 1992 and 1999, respectively, and was promoted to Associate Professor in 2012 before advancing to full Professor. Education: BSc in Statistics, AUEB (1992) PhD in Applied Statistics, AUEB (1999) His research spans computational statistics, mixture models, EM algorithms, copulas, multivariate discrete data, and applications in sports, insurance, and seismicity. He has published extensively in top-tier statistical journals such as the Journal of the Royal Statistical Society and Statistics in Medicine . The 15 most recent publications reveal a strong focus on multivariate count data, integer-valued time series, model-based clustering using copulas, and applications in actuarial science and health. His work frequently involves mixture models, Bayesian inference, and innovative extensions of Poisson-based frameworks. Scientific Service and Recognition: Associate Editor: Metron, Communications in Statistics, IMA Journal of Management Mathematics, Stochastic Environmental Research and Risk Assessment Editor: Biometrics Bulletin of IBS Member: American Statistical Society, International Statistical Institute, International Association of Statistical Computing, Hellenic Statistical Institute Publicity Officer: Eastern Mediterranean Region, International Biometric Society Advising and Grants: He has supervised 4 completed PhDs and 18 Master’s theses, with several more in progress. He has led and participated in research projects funded by the European Union and EUROSTAT, particularly in official statistics. His advising spans methodological and applied topics in statistics. Labs and Teams: While no formal lab is named, he collaborates extensively with researchers in actuarial science, transportation, biostatistics, and environmental risk, often through joint projects and publications.
Prof. Dr. Antonis Chatzinotas is a leading figure in Microbial Interaction Ecology at the Helmholtz Centre for Environmental Research (UFZ) and holds a Professorship at the University of Leipzig since 2020. His research focuses on understanding microbial and viral interactions in terrestrial and aquatic ecosystems, particularly how environmental changes affect community composition, genetic landscapes, and biogeochemical cycles. Head of Microbial Interaction Ecology group at UFZ Professor at University of Leipzig Key research areas include: Microbial and viral diversity in pristine/aquifer systems Predatory interactions between protists/bacteria/viruses Applications in agroecosystems and environmental biotechnology Functional redundancy in microbial communities Impact of environmental variability on coexistence Recent publications highlight interdisciplinary approaches combining DNA stable isotope probing , metagenomics , and ecological modeling to study bioaugmentation, pesticide-microbe interactions, and viral transport mechanisms. His work bridges theoretical ecology with practical environmental solutions. Collaborations span institutions like EPFL, ETH Zurich, and University of Otago. Current projects explore low-risk biopesticides, virome-land use relationships, and microbial stability-vegetation links. The group actively supports open science principles and participates in transformative publishing agreements.
Hugo Georges Victor Lavenant serves as Assistant Professor in the Department of Decision Sciences at Bocconi University, Milan, where he has held a faculty position since 2020. Previously, he completed a postdoctoral fellowship at the University of British Columbia (2019-2020) under the Pacific Institute of Mathematical Sciences and earned his PhD in Mathematics from Université Paris-Sud (2016-2019) under Filippo Santambrogio's supervision. His academic foundation includes: PhD in Mathematics, Université Paris-Sud (2016-2019) Studies at École Normale Supérieure (2012-2016) covering mathematics, physics, history, and philosophy of science Classes préparatoires in mathematics and physics (2010-2012) Lavenant's research centers on optimal transport theory and its applications across mathematical disciplines. He investigates geometric structures in Wasserstein spaces, develops numerical methods for dynamical optimal transport, and bridges theoretical advances with Bayesian statistics. His work demonstrates particular innovation in trajectory inference for biological data and dependence measures for random measures, connecting pure mathematics with computational statistics. Recent publications reveal accelerating interdisciplinary impact, with 2024-2025 works extending optimal transport to machine learning (kernel methods, variational inference) and data science (opinion dynamics, single-cell analysis). This trajectory shows increasing methodological sophistication in handling measure-valued mappings and non-smooth geometries while maintaining computational tractability. Award recognition includes: Pacific Institute of Mathematical Sciences Postdoctoral Fellowship Lavenant actively mentors early-career researchers through formal advising relationships and collaborative projects. He currently supervises two PhD candidates (George Kanchaveli and Francesco Mascari, co-advised with Marta Catalano) and has guided Master's students including Mathis Hardion and Niccolò Bargellini. His teaching portfolio spans advanced analysis, optimization, and real analysis courses at Bocconi, reflecting his commitment to mathematical rigor in education. He operates within Bocconi's Decision Sciences ecosystem while maintaining international collaborations with researchers at UBC, Université Paris-Sud, and statistical groups worldwide. Current projects focus on entropy-based transport methods and geometric approaches to nonparametric statistics, positioning his work at the intersection of theoretical mathematics and data-driven applications.
Daumantas Bloznelis is an Associate Professor of Business Analytics at the Norwegian University of Life Sciences (Ås, Norway) and an Adjunct Associate Professor at the University of Inland Norway (Rena, Norway). He holds a PhD in Economics from the Norwegian University of Life Sciences, with visiting scholar experience at Cornell University (USA). His research focuses on financial econometrics, commodity markets, and statistical price modeling, with particular emphasis on risk management and forecasting in aquaculture sectors. Bloznelis has extensive experience in academia, including teaching courses on machine learning, econometrics, and quantitative methods across multiple universities. He has supervised numerous PhD and Master’s theses, contributing to the development of future scholars in finance and management. His work also extends to applied research, such as cross-hedging carbon risk and portfolio optimization in electric vehicle sectors. Bloznelis has received several accolades, including scholarships from the Norwegian Research Council and Vilnius University, and awards for academic excellence in Lithuania. Education: PhD in Economics/Finance (2011–2016), Norwegian University of Life Sciences MSc in Statistics/Econometrics (2009–2011), Vilnius University BSc in Statistics/Econometrics (2005–2009), Vilnius University Research Interests: Bloznelis specializes in statistical price modeling, forecasting methodologies, and risk management in financial and commodity markets. His work integrates machine learning and econometric techniques to address practical challenges in sectors like salmon farming and electric vehicles. He also explores the application of copula models and factor analysis to portfolio optimization and market dynamics. Key Awards: 3rd prize in International Econometric Team Competition (2010) PRESIDENT OF LITHUANIA AWARD for dictation contest (2007) Prime Minister of Lithuania Award for matriculation excellence (2005) Professional Contributions: Bloznelis has presented at over 30 international conferences, including NCCC commodity price analysis meetings and CEMA annual conferences. He serves on the Board of Advisors for Vilnius University’s Faculty of Mathematics and Informatics. His research outputs include influential papers on futures market biases, hedging strategies, and factor models in commodity pricing.
Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.
Hyungsik Roger Moon is Professor of Economics in the Department of Economics at the University of Southern California's Dornsife College of Letters, Arts and Sciences, where he has served since 2000 after beginning his career at UC Santa Barbara. His academic trajectory progressed from Assistant Professor (2000) to Associate Professor (2004) and full Professor (2008), reflecting sustained contributions to econometric methodology. His educational foundation includes: Ph.D. in Economics, Yale University, 1998 M.A. in Economics, Yale University, 1995 B.A. in Economics, Seoul National University, 1989 Moon's research centers on econometric theory development and applied methodology, with particular expertise in panel data analysis, dynamic modeling, and high-dimensional estimation. His theoretical innovations address complex challenges in interactive fixed effects, unit root testing, and heterogeneity modeling, while applied work spans labor economics (income dynamics), health economics (pancreatic cancer trials), and macroeconomics (Covid-19 forecasting). This dual focus bridges rigorous mathematical frameworks with real-world policy applications across multiple economic subfields. Analysis of recent publications reveals an intensifying focus on robust estimation techniques for dyadic data, Bayesian approaches to sparse heterogeneity, and methodological innovations in forecasting with censored panel data. His work increasingly integrates machine learning concepts with traditional econometrics, particularly in high-dimensional seemingly unrelated regression systems and network-based peer effect modeling. His distinguished scientific contributions have been recognized through: Fellow of the Econometric Society (2023) Fellow of the Journal of Econometrics (2019) RK Cho Economics Award (2018) Maekyung/KAEA Economist Award (2012) Econometric Theory Multa Scripsit Award (2006-2007) Korea-America Economic Association Young Scholar Award (2005) Moon has secured significant research funding including an NSF grant of $180,675 for 'Forecasting with Dynamic Panel Data Models' (2016-2020) and $68,000 for 'Asymptotic Analysis of Panel Regression Models' (2009-2010). His academic leadership extends to editorial roles at the Journal of Business and Economic Statistics, Econometric Theory, and Journal of Econometrics, plus administrative service as Director of Graduate Studies for USC's Economics Ph.D. program (2018-2021) and Associate Director of USC Dornsife INET (2015-2017). Through his position at USC Dornsife INET and graduate program leadership, Moon actively shapes research directions in new economic thinking while mentoring future econometricians through advanced courses like Big Data Econometrics.
Volkert Paulsen is a Senior Lecturer at the Institute of Mathematical Stochastics at the University of Münster. His career spans institutions including the University of Kiel, where he completed his Habilitation (2000), Dissertation (1994), and Diplomarbeit (1989). He has taught extensively in Financial Mathematics , Stochastic Analysis , and Mathematical Statistics , supervising over 50 Bachelor, Master, and Diploma theses on topics such as risk modeling, portfolio optimization, and derivative valuation. Research Interests: Paulsen's work focuses on Financial Mathematics (continuous-time models, American options, unit-linked insurance), Stochastic Analysis (optimal stopping, martingale methods), and Risk Modeling (credit risk, extreme value statistics). His publications include foundational studies on nonlinear observation costs in optimal stopping problems and stochastic approaches to portfolio management. Scientific Contributions: His research spans journal articles in Stochastic Processes and their Applications and Journal of Applied Probability , with recent seminar topics covering Lévy Processes , Copula Modeling , and Stochastic Volatility . He employs R for statistical applications and integrates mathematical theory with practical finance and insurance contexts. Contact: Email: Volkert.Paulsen@uni-muenster.de Room: 130.010, Orléans-Ring 10, 48149 Münster Phone: +49 251 83-33771
Roberto Ghiselli Ricci is a Full Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Informatics and Statistics. His academic career includes extensive teaching and research in mathematical statistics and probability, with a focus on copula theory, aggregation functions, and econometric applications. He currently teaches courses such as Calculus, Linear Algebra, and Mathematics for Environmental Sciences. His research explores advanced topics in probability theory, including copula properties, fixed-point theorems, and axiomatic characterizations of mobility measures. Recent publications highlight contributions to fuzzy set theory, optimization penalties, and financial securities modeling. His work bridges mathematical rigor with practical applications in economics and environmental policy analysis. Publications trends emphasize interdisciplinary approaches, with notable contributions to Fuzzy Sets and Systems , International Journal of Game Theory , and Social Choice and Welfare . He actively participates in academic activities through courses, research collaborations, and advisory roles within his department.
Vicky Fasen-Hartmann is a Professor at the Karlsruhe Institute of Technology (KIT) within the Department of Mathematics, specifically affiliated with the Institute of Stochastics. She has held her W3 Professor position since October 2012, with two periods of parental leave (August 2016-August 2017 and October 2018-October 2019). Prior to her current position, she held postdoctoral research positions at ETH Zurich (RiskLab), TU Munich, Université Pierre et Marie Curie, and Cornell University. Her educational background includes: Habilitation (2010) in Heavy Tails in Finance, Insurance and Telecommunication from TU Munich Ph.D. (2004) in Extremes of Lévy Driven Moving Average Processes with Applications in Finance from TU Munich Diploma in Mathematics (2002) from Karlsruhe Institute of Technology Professor Fasen-Hartmann's research spans multiple areas of theoretical and applied statistics with a focus on extreme value theory, heavy-tailed distributions, and their applications in finance and risk management. Her work bridges theoretical probability with practical financial applications, particularly in modeling rare events and systemic risks. She has made significant contributions to the understanding of Lévy processes, continuous-time ARMA models, and multivariate extremes. Her research combines rigorous mathematical theory with practical applications in financial mathematics, insurance, and telecommunications networks. The trends in her recent publications (2020-2025) show a clear evolution toward high-dimensional extreme value theory, financial network risk contagion, and advanced modeling of continuous-time processes. Her work increasingly addresses the challenges of modern financial systems, including systemic risk measurement, high-dimensional dependency structures, and the statistical properties of extreme events in complex systems. She has developed innovative methodologies for analyzing multivariate extremes, risk contagion, and continuous-time state space models. Professor Fasen-Hartmann has served in significant editorial roles including Associate Editor for the Scandinavian Journal of Statistics since 2014, Managing Editor of Lévy Matters (2008-2014), and Editor of Bernoulli News (2009-2011). She has also been active in academic service through committee work, including the Steering Committee of the Probability and Statistics Group in Germany (2014-2016) and the Examination Board of the Department of Mathematics at KIT (since 2017). She has supervised numerous doctoral and master's students, with current PhD candidates including Lucas Butsch (since 2021) and previously Lea Schenk, Celeste Mayer, Markus Scholz, and Sebastian Kimmig. Her teaching portfolio includes advanced courses in Time Series Analysis, Continuous Time Finance, Extreme Value Theory, and Asymptotic Stochastics. She regularly organizes workshops and conferences on specialized topics in probability and statistics, demonstrating her leadership in the academic community.
Andrew J. Patton is the Zelter Family Distinguished Professor and Professor of Economics at Duke University's Trinity College of Arts & Sciences, holding these roles since 2016 and 2013 respectively. Previously, he served as Associate Professor of Economics at Duke from 2009 to 2013. Patton holds a Ph.D. and M.A. from the University of California, San Diego (2002 and 2000), and a B.Bus. from the University of Technology Sydney (1998). His research focuses on financial econometrics, particularly volatility forecasting, dependence modeling, high-frequency financial data analysis, and hedge fund dynamics. He has published extensively in top journals such as the Journal of Finance , Journal of Financial Economics , and Econometrica . His recent work includes advancements in realized semicovariances, k-means clustering for unobserved heterogeneity, and risk price variations. Patton’s 2016 Distinguished Professorship reflects his scholarly impact. His research emphasizes practical applications, such as improving volatility forecasts and addressing market anomalies’ trading costs.
